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<h3>Feature Extraction</h3><br>
we used a modified version of the supplied AudioFeatureExtractor in order to allow for document identification by file name.<br>
the resulting ARFF files can be downloaded here:<br>
<a href="http://web.student.tuwien.ac.at/~e0625597/ISMIRrhythm_features.zip">ISMIR features</a><br>
<a href="http://web.student.tuwien.ac.at/~e0625597/GTZAN_features.zip">GTZAN features</a>


<h3>Similarity Retrieval</h3><br>
It appears that we misunderstood the instructions in assignment 2. The sentence "i.e., retrieve a list of similar documents based on each index used, and present the retrieval results to the user in a compact form (i.e., not simply displaying all lists after each other)."
lead us to believe that a merged result list, such as discribed in assignment 3, was already required then.<br> We decided to display the k results with minimal distance across all specified feature files. 
We now think that the phrase "compact form" just means that we are required to provide links to the full result list for each index. <br> We added this feature to the output of assignment 3. <br>
Furthermore we adjusted the means by which we are selecting the kBest results in our merged output. By using the average rank we can ensure that each index file has equal influence on the merged List. <br>
A problem in the merged list we gave in assignment 2 was that if a feature file is more "dense" in nature, calculated distances are bound to be smaller than in a feature file that is less "dense". By selecting the minimal distance across all feature files, information of less "dense" feature files would be ignored.<br>
<center>10 Random Queries:<br><table border>
<tr><td>GTZAN</td><td>ISMIR</td></tr>
<tr><td><a href="output_blues_blues.00014.mp3.html">blues.00014.mp3</a></td><td><a href="output_Jive_Jive_103814.mp3.html">Jive_103814.mp3<a></td></tr>
<tr><td><a href="output_disco_disco.00037.mp3.html">disco.00037.mp3</a></td><td><a href="output_Rumba_Rumba_103609.mp3.html">Rumba_103609.mp3<a></td></tr>
<tr><td><a href="output_jazz_jazz.00038.mp3.html">jazz.00038.mp3</a></td><td><a href="output_SlowWaltz_SlowWaltz_104501.mp3.html">SlowWaltz_104501.mp3<a></td></tr>
<tr><td><a href="output_reggae_reggae.00038.mp3.html">reggae.00038</a></td><td><a href="output_Tango_Tango_104907.mp3.html">Tango_104907.mp3<a></td></tr>
<tr><td><a href="output_rock_rock.00038.mp3.html">rock.00038.mp3</a></td><td><a href="output_VienneseWaltz_VienneseWaltz_104409.mp3.html">VienneseWaltz_104409.mp3<a></td></tr>
</table>
</center><br>
When inspecting the merged result lists of these examples we feel reassured, that our choice of merging along the average rank is indeed correct. Several entrys of the merged result list are not contained in the result list of any singular Feature set, but in the combination of sets they are superior.
Furthermore we can see, that SSD generates a lot less distance between features than RH and RP.<BR><br>
In addition the assignment asked for statistical analysis across all documents in a corpus. These can be found here.<br><br>
<center>Global Querys<br><table border>
<tr>
<td><a href="output_GTZAN_GlobalQuery.html">GTZAN</a></td>
<td><a href="output_IMIR_GlobalQuery.html">IMIR</a></td>
</tr>
</table></center>
These results were obtaind by calculation of a distance matrix for each feature set, which allowed us to find statistical information(min max avg) for each such set. 
Furthermore, from these distance matrices we were abel to obtain ranking matrices in which each row in a matrix was filled with the appropriate rank in respect to the approriate column. 
Finally, from those ranking matrices we constructed a merged rank matrix which simply computed the average rank across all feature sets and ranked this average once more. 
This way we were able to exactly determin how many documents in the corpus can query a particular document and how many documents in each feature set can query the document in question. 



<h3>Genre Classification</h3><br>
WEKA experimenter cannot handle String attributes. Thus we used the standard AudioFeatureExtractor.
GTZAN knn = 3,1,5 / weight distance = no, 1/d, 1-d / 
SMO c = 1.0,2.0,3.0 / kernel = polykernel,normalizedPolyKernel, PUK
RndForest trees = 10,15,20 / seed 1,2,1 / numFeatures 0,0,1

GTZAN knn = 3,1,5 / weight distance = no, 1/d, 1-d / 
SMO c = 1.0,2.0,1.0 / numFolds -1, -1, 5
RndForest trees = 10,15,20 / seed 1,2,1 / numFeatures 0,0,1
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